A multiple criteria group decision making model with entropy weight in an intuitionistic fuzzy environment

Chia Chang Hung, Liang-Hsuan Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The theory of intuitionistic fuzzy sets (IFSs) is well-suited to dealing with vagueness and hesitancy. In this study, we propose a new fuzzy TOPSIS group decision making model using entropy weight for dealing with multiple criteria decision making (MCDM) problems in an intuitionistic fuzzy environment. This model can measure the degrees of satisfaction and dissatisfaction of each alternative evaluated across a set of criteria. To obtain the weighted fuzzy decision matrix, we employ the concept of Shannon's entropy to calculate the criteria weights. An investment example is used to illustrate the application of the proposed model.

Original languageEnglish
Title of host publicationIntelligent Automation and Computer Engineering
Pages17-26
Number of pages10
DOIs
Publication statusPublished - 2010 Dec 1
EventInternational Conference in Intelligent Automation and Computer Engineering, Under the Auspices of the International MultiConference of Engineers and Computer Scientists, IMECS 2009 - Hong Kong, Hong Kong
Duration: 2009 Mar 182009 Mar 20

Publication series

NameLecture Notes in Electrical Engineering
Volume52 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Other

OtherInternational Conference in Intelligent Automation and Computer Engineering, Under the Auspices of the International MultiConference of Engineers and Computer Scientists, IMECS 2009
CountryHong Kong
CityHong Kong
Period09-03-1809-03-20

Fingerprint

Entropy
Decision making
Fuzzy sets

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

Hung, C. C., & Chen, L-H. (2010). A multiple criteria group decision making model with entropy weight in an intuitionistic fuzzy environment. In Intelligent Automation and Computer Engineering (pp. 17-26). (Lecture Notes in Electrical Engineering; Vol. 52 LNEE). https://doi.org/10.1007/978-90-481-3517-2-2
Hung, Chia Chang ; Chen, Liang-Hsuan. / A multiple criteria group decision making model with entropy weight in an intuitionistic fuzzy environment. Intelligent Automation and Computer Engineering. 2010. pp. 17-26 (Lecture Notes in Electrical Engineering).
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Hung, CC & Chen, L-H 2010, A multiple criteria group decision making model with entropy weight in an intuitionistic fuzzy environment. in Intelligent Automation and Computer Engineering. Lecture Notes in Electrical Engineering, vol. 52 LNEE, pp. 17-26, International Conference in Intelligent Automation and Computer Engineering, Under the Auspices of the International MultiConference of Engineers and Computer Scientists, IMECS 2009, Hong Kong, Hong Kong, 09-03-18. https://doi.org/10.1007/978-90-481-3517-2-2

A multiple criteria group decision making model with entropy weight in an intuitionistic fuzzy environment. / Hung, Chia Chang; Chen, Liang-Hsuan.

Intelligent Automation and Computer Engineering. 2010. p. 17-26 (Lecture Notes in Electrical Engineering; Vol. 52 LNEE).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - The theory of intuitionistic fuzzy sets (IFSs) is well-suited to dealing with vagueness and hesitancy. In this study, we propose a new fuzzy TOPSIS group decision making model using entropy weight for dealing with multiple criteria decision making (MCDM) problems in an intuitionistic fuzzy environment. This model can measure the degrees of satisfaction and dissatisfaction of each alternative evaluated across a set of criteria. To obtain the weighted fuzzy decision matrix, we employ the concept of Shannon's entropy to calculate the criteria weights. An investment example is used to illustrate the application of the proposed model.

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Hung CC, Chen L-H. A multiple criteria group decision making model with entropy weight in an intuitionistic fuzzy environment. In Intelligent Automation and Computer Engineering. 2010. p. 17-26. (Lecture Notes in Electrical Engineering). https://doi.org/10.1007/978-90-481-3517-2-2